GraphCanon updated 3w · GitHub synced 3w
Decision brief
dbt-mcp offers a specialized MCP server for dbt interaction, supporting data analytics and engineering tasks in Python under the Apache-2.0 license.
Good fit when
- You are working with dbt and need to implement the Model Context Protocol (MCP) for more precise project management.
- Your team prefers Python as the programming language for deploying MCP servers connected to dbt projects.
Avoid when
- If your project does not require interaction through the Model Context Protocol with dbt, opting for a generic data processing tool might be simpler and less resource-intensive.
- When the primary goal is model training or prediction without focusing on dbt workflow management via MCP, alternative tools may serve better.
- Pricing:
- freemium - Freely available with no direct cost, ideal for projects without budget constraints but seeking flexibility.
- Requirements:
- Ensure you have Python and dbt installed to utilize dbt-mcp effectively.; Advanced knowledge of data engineering and analytics best practices is required to make the most out of dbt-mcp's MCP capabilities.
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (2d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install dbt-mcp PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
dbt-labs/dbt-mcp provides an implementation of the Model Context Protocol (MCP) server designed to facilitate interactions with dbt.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 27, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 27, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 27, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 27, 2026
Categories
Tags
README
dbt MCP Server
This MCP (Model Context Protocol) server provides various tools to interact with dbt. You can use this MCP server to provide AI agents with context of your project in dbt Core, dbt Fusion, and dbt Platform.
Read our documentation here to learn more. This blog post provides more details for what is possible with the dbt MCP server.
Experimental MCP Bundle
We publish an experimental Model Context Protocol Bundle (dbt-mcp.mcpb) with each release so that MCPB-aware clients can import this server without additional setup. Download the bundle from the latest release assets and follow Anthropic's mcpb CLI docs to install or inspect it.
Feedback
If you have comments or questions, create a GitHub Issue or join us in the community Slack in the #tools-dbt-mcp channel.
Architecture
The dbt MCP server architecture allows for your agent to connect to a variety of tools.
Tools
SQL
Tools for executing and generating SQL on dbt Platform infrastructure.
execute_sql: Executes SQL on dbt Platform infrastructure with Semantic Layer support.text_to_sql: Generates SQL from natural language using project context.
Semantic Layer
To learn more about the dbt Semantic Layer, click here.
get_dimension_values: Gets distinct values for a dimension; option to scope to specific metrics.get_dimensions: Gets dimensions for specified metrics.get_entities: Gets entities for specified metrics.get_metrics_compiled_sql: Returns compiled SQL for metrics without executing the query.list_metrics: Retrieves all defined metrics.list_saved_queries: Retrieves all saved queries.query_metrics: Executes metric queries with filtering and grouping options.
Discovery
To learn more about the dbt Discovery API, click here.
get_all_macros: Retrieves macros; option to filter by package or return package names only.get_all_models: Retrieves name and description of all models.get_all_sources: Gets all sources with freshness status; option to filter by source name.get_exposure_details: (deprecated — useget_node_detailsinstead)get_exposures: Gets all exposures (downstream dashboards, apps, or analyses).get_lineage: Gets full lineage graph (ancestors and descendants) with type and depth filtering.get_macro_details: (deprecated — useget_node_detailsinstead)get_mart_models: Retrieves all mart models.get_model_children: (deprecated — useget_lineageinstead)get_model_details: (deprecated — useget_node_detailsinstead)get_model_health: Gets health signals: run status, test results, and upstream source freshness.get_model_parents: (deprecated — useget_lineageinstead)get_model_performance: Gets execution history for a model; option to include test results.get_node_details: Gets full details for any dbt resource type (model, source, exposure, test, seed, snapshot, macro, semantic_model).get_related_models: Finds similar models using semantic search.get_seed_details: (deprecated — useget_node_detailsinstead)get_semantic_model_details: (deprecated — useget_node_detailsinstead)get_snapshot_details: (deprecated — useget_node_detailsinstead)get_source_details: (deprecated — useget_node_detailsinstead)get_test_details: (deprecated — useget_node_detailsinstead)search: [Alpha] Searches for resources across the dbt project (not generally available).
dbt CLI
Allowing your client to utilize dbt commands through the MCP tooling could modify your data models, sources, and warehouse objects. Proceed only if yo
For agents
This page has a .md twin and JSON over the API.